layer_activation_selu: Scaled Exponential Linear Unit.

In dfalbel/keras: R Interface to 'Keras'

Description

SELU is equal to: scale * elu(x, alpha), where alpha and scale are pre-defined constants.

Usage

layer_activation_selu(
  object,
  input_shape = NULL,
  batch_input_shape = NULL,
  batch_size = NULL,
  dtype = NULL,
  name = NULL,
  trainable = NULL,
  weights = NULL
)

Arguments

object	Model or layer object
input_shape	Input shape (list of integers, does not include the samples axis) which is required when using this layer as the first layer in a model.
batch_input_shape	Shapes, including the batch size. For instance, batch_input_shape=c(10, 32) indicates that the expected input will be batches of 10 32-dimensional vectors. batch_input_shape=list(NULL, 32) indicates batches of an arbitrary number of 32-dimensional vectors.
batch_size	Fixed batch size for layer
dtype	The data type expected by the input, as a string (float32, float64, int32...)
name	An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
trainable	Whether the layer weights will be updated during training.
weights	Initial weights for layer.

Details

The values of alpha and scale are chosen so that the mean and variance of the inputs are preserved between two consecutive layers as long as the weights are initialized correctly (see initializer_lecun_normal) and the number of inputs is "large enough" (see article for more information).

Note:

• To be used together with the initialization "lecun_normal".

• To be used together with the dropout variant "AlphaDropout".

See Also

Self-Normalizing Neural Networks, initializer_lecun_normal, layer_alpha_dropout

Other activation layers: layer_activation_elu(), layer_activation_leaky_relu(), layer_activation_parametric_relu(), layer_activation_relu(), layer_activation_softmax(), layer_activation_thresholded_relu(), layer_activation()

dfalbel/keras documentation built on Nov. 27, 2019, 8:16 p.m.